2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

Technical Program

CI-2: Computational Imaging for Inverse Problems

Session Type: Poster
Time: Wednesday, 9 June, 15:30 - 16:15
Location: Gather.Town
Session Chair: Saiprasad Ravishankar, Michigan State University
 
CI-2.1: STOCHASTIC DEEP UNFOLDING FOR IMAGING INVERSE PROBLEMS
         Jiaming Liu; Washington University in St. Louis
         Yu Sun; Washington University in St. Louis
         Weijie Gan; Washington University in St. Louis
         Xiaojian Xu; Washington University in St. Louis
         Brendt Wohlberg; Los Alamos National Laboratory
         Ulugbek Kamilov; Washington University in St. Louis
 
CI-2.2: FUSION-BASED DIGITAL IMAGE CORRELATION FRAMEWORK FOR STRAIN MEASUREMENT
         Laixi Shi; Carnegie Mellon University
         Dehong Liu; Mitsubishi Electric Research Laboratories (MERL)
         Masaki Umeda; Mitsubishi Electric
         Norihiko Hana; Mitsubishi Electric
 
CI-2.3: LEARNING SPARSIFYING TRANSFORMS FOR IMAGE RECONSTRUCTION IN ELECTRICAL IMPEDANCE TOMOGRAPHY
         Kaiyi Yang; Nanyang Technological University
         Narong Borijindargoon; Nanyang Technological University
         Boon Poh Ng; Nanyang Technological University
         Saiprasad Ravishankar; Michigan State University
         Bihan Wen; Nanyang Technological University
 
CI-2.4: D-VDAMP: DENOISING-BASED APPROXIMATE MESSAGE PASSING FOR COMPRESSIVE MRI
         Christopher Metzler; Stanford University
         Gordon Wetzstein; Stanford University
 
CI-2.5: EMPIRICALLY ACCELERATING SCALED GRADIENT PROJECTION USING DEEP NEURAL NETWORK FOR INVERSE PROBLEMS IN IMAGE PROCESSING
         Byung Hyun Lee; UNIST
         Se Young Chun; Seoul National University
 
CI-2.6: SYNTHETIC APERTURE ACOUSTIC IMAGING WITH DEEP GENERATIVE MODEL BASED SOURCE DISTRIBUTION PRIOR
         Boqiang Fan; Rice University
         Samarjit Das; Bosch Research Pittsburgh